import torch import itertools from util.image_pool import ImagePool from .base_model import BaseModel from . import networks class CycleGANModel(BaseModel): """ This class implements the CycleGAN model, for learning image-to-image translation without paired data. The model training requires '--dataset_mode unaligned' dataset. By default, it uses a '--netG resnet_9blocks' ResNet generator, a '--netD basic' discriminator (PatchGAN introduced by pix2pix), and a least-square GANs objective ('--gan_mode lsgan'). CycleGAN paper: https://arxiv.org/pdf/1703.10593.pdf """ @staticmethod def modify_commandline_options(parser, is_train=True): """Add new dataset-specific options, and rewrite default values for existing options. Parameters: parser -- original option parser is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options. Returns: the modified parser. For CycleGAN, in addition to GAN losses, we introduce lambda_A, lambda_B, and lambda_identity for the following losses. A (source domain), B (target domain). Generators: G_A: A -> B; G_B: B -> A. Discriminators: D_A: G_A(A) vs. B; D_B: G_B(B) vs. A. Forward cycle loss: lambda_A * ||G_B(G_A(A)) - A|| (Eqn. (2) in the paper) Backward cycle loss: lambda_B * ||G_A(G_B(B)) - B|| (Eqn. (2) in the paper) Identity loss (optional): lambda_identity * (||G_A(B) - B|| * lambda_B + ||G_B(A) - A|| * lambda_A) (Sec 5.2 "Photo generation from paintings" in the paper) Dropout is not used in the original CycleGAN paper. """ parser.set_defaults(no_dropout=True) # default CycleGAN did not use dropout if is_train: parser.add_argument('--lambda_A', type=float, default=10.0, help='weight for cycle loss (A -> B -> A)') parser.add_argument('--lambda_B', type=float, default=10.0, help='weight for cycle loss (B -> A -> B)') parser.add_argument('--lambda_identity', type=float, default=0.5, help='use identity mapping. Setting lambda_identity other than 0 has an effect of scaling the weight of the identity mapping loss. For example, if the weight of the identity loss should be 10 times smaller than the weight of the reconstruction loss, please set lambda_identity = 0.1') return parser def __init__(self, opt): """Initialize the CycleGAN class. Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions """ BaseModel.__init__(self, opt) # specify the training losses you want to print out. The training/test scripts will call self.loss_names = ['D_A', 'G_A', 'cycle_A', 'idt_A', 'D_B', 'G_B', 'cycle_B', 'idt_B'] # specify the images you want to save/display. The training/test scripts will call visual_names_A = ['real_A', 'fake_B', 'rec_A'] visual_names_B = ['real_B', 'fake_A', 'rec_B'] if self.isTrain and self.opt.lambda_identity > 0.0: # if identity loss is used, we also visualize idt_B=G_A(B) ad idt_A=G_A(B) visual_names_A.append('idt_B') visual_names_B.append('idt_A') self.visual_names = visual_names_A + visual_names_B # combine visualizations for A and B # specify the models you want to save to the disk. The training/test scripts will call and . if self.isTrain: self.model_names = ['G_A', 'G_B', 'D_A', 'D_B'] else: # during test time, only load Gs self.model_names = ['G_A', 'G_B'] # define networks (both Generators and discriminators) # The naming is different from those used in the paper. # Code (vs. paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X) self.netG_A = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids) self.netG_B = networks.define_G(opt.output_nc, opt.input_nc, opt.ngf, opt.netG, opt.norm, not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids) if self.isTrain: # define discriminators self.netD_A = networks.define_D(opt.output_nc, opt.ndf, opt.netD, opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids) self.netD_B = networks.define_D(opt.input_nc, opt.ndf, opt.netD, opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids) if self.isTrain: if opt.lambda_identity > 0.0: # only works when input and output images have the same number of channels assert(opt.input_nc == opt.output_nc) self.fake_A_pool = ImagePool(opt.pool_size) # create image buffer to store previously generated images self.fake_B_pool = ImagePool(opt.pool_size) # create image buffer to store previously generated images # define loss functions self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device) # define GAN loss. self.criterionCycle = torch.nn.L1Loss() self.criterionIdt = torch.nn.L1Loss() # initialize optimizers; schedulers will be automatically created by function . self.optimizer_G = torch.optim.Adam(itertools.chain(self.netG_A.parameters(), self.netG_B.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999)) self.optimizer_D = torch.optim.Adam(itertools.chain(self.netD_A.parameters(), self.netD_B.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999)) self.optimizers.append(self.optimizer_G) self.optimizers.append(self.optimizer_D) def set_input(self, input): """Unpack input data from the dataloader and perform necessary pre-processing steps. Parameters: input (dict): include the data itself and its metadata information. The option 'direction' can be used to swap domain A and domain B. """ AtoB = self.opt.direction == 'AtoB' self.real_A = input['A' if AtoB else 'B'].to(self.device) self.real_B = input['B' if AtoB else 'A'].to(self.device) self.image_paths = input['A_paths' if AtoB else 'B_paths'] def forward(self): """Run forward pass; called by both functions and .""" self.fake_B = self.netG_A(self.real_A) # G_A(A) self.rec_A = self.netG_B(self.fake_B) # G_B(G_A(A)) self.fake_A = self.netG_B(self.real_B) # G_B(B) self.rec_B = self.netG_A(self.fake_A) # G_A(G_B(B)) def backward_D_basic(self, netD, real, fake): """Calculate GAN loss for the discriminator Parameters: netD (network) -- the discriminator D real (tensor array) -- real images fake (tensor array) -- images generated by a generator Return the discriminator loss. We also call loss_D.backward() to calculate the gradients. """ # Real pred_real = netD(real) loss_D_real = self.criterionGAN(pred_real, True) # Fake pred_fake = netD(fake.detach()) loss_D_fake = self.criterionGAN(pred_fake, False) # Combined loss and calculate gradients loss_D = (loss_D_real + loss_D_fake) * 0.5 loss_D.backward() return loss_D def backward_D_A(self): """Calculate GAN loss for discriminator D_A""" fake_B = self.fake_B_pool.query(self.fake_B) self.loss_D_A = self.backward_D_basic(self.netD_A, self.real_B, fake_B) def backward_D_B(self): """Calculate GAN loss for discriminator D_B""" fake_A = self.fake_A_pool.query(self.fake_A) self.loss_D_B = self.backward_D_basic(self.netD_B, self.real_A, fake_A) def backward_G(self): """Calculate the loss for generators G_A and G_B""" lambda_idt = self.opt.lambda_identity lambda_A = self.opt.lambda_A lambda_B = self.opt.lambda_B # Identity loss if lambda_idt > 0: # G_A should be identity if real_B is fed: ||G_A(B) - B|| self.idt_A = self.netG_A(self.real_B) self.loss_idt_A = self.criterionIdt(self.idt_A, self.real_B) * lambda_B * lambda_idt # G_B should be identity if real_A is fed: ||G_B(A) - A|| self.idt_B = self.netG_B(self.real_A) self.loss_idt_B = self.criterionIdt(self.idt_B, self.real_A) * lambda_A * lambda_idt else: self.loss_idt_A = 0 self.loss_idt_B = 0 # GAN loss D_A(G_A(A)) self.loss_G_A = self.criterionGAN(self.netD_A(self.fake_B), True) # GAN loss D_B(G_B(B)) self.loss_G_B = self.criterionGAN(self.netD_B(self.fake_A), True) # Forward cycle loss || G_B(G_A(A)) - A|| self.loss_cycle_A = self.criterionCycle(self.rec_A, self.real_A) * lambda_A # Backward cycle loss || G_A(G_B(B)) - B|| self.loss_cycle_B = self.criterionCycle(self.rec_B, self.real_B) * lambda_B # combined loss and calculate gradients self.loss_G = self.loss_G_A + self.loss_G_B + self.loss_cycle_A + self.loss_cycle_B + self.loss_idt_A + self.loss_idt_B self.loss_G.backward() def optimize_parameters(self): """Calculate losses, gradients, and update network weights; called in every training iteration""" # forward self.forward() # compute fake images and reconstruction images. # G_A and G_B self.set_requires_grad([self.netD_A, self.netD_B], False) # Ds require no gradients when optimizing Gs self.optimizer_G.zero_grad() # set G_A and G_B's gradients to zero self.backward_G() # calculate gradients for G_A and G_B self.optimizer_G.step() # update G_A and G_B's weights # D_A and D_B self.set_requires_grad([self.netD_A, self.netD_B], True) self.optimizer_D.zero_grad() # set D_A and D_B's gradients to zero self.backward_D_A() # calculate gradients for D_A self.backward_D_B() # calculate graidents for D_B self.optimizer_D.step() # update D_A and D_B's weights